Clinician preferences for neurotechnologies in pediatric drug‐resistant epilepsy: A discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: Novel and minimally invasive neurotechnologies offer the potential to reduce the burden of epilepsy while avoiding the risks of conventional resective surgery. Few neurotechnologies have been tested in randomized controlled trials with pediatric populations, leaving clinicians to face decisions about whether to recommend these treatments with insufficient evidence about the relevant risks and benefits. This study specifically explores the preferences of clinicians for treating pediatric drug-resistant epilepsy (DRE) with novel neurotechnologies. METHODS: A discrete-choice experiment (DCE) was designed to elicit the preferences of clinicians with experience in treating children with DRE using novel neurotechnological interventions. The preferences for six key attributes used when making treatment decisions (chances of clinically significant improvement in seizures, major and minor risks from intervention, availability of evidence, financial burden for the family, and access to the intervention) were estimated using a conditional logit model. The estimates from this model were then used to predict the adoption of existing novel neurotechnological interventions. RESULTS: Sixty-eight clinicians completed the survey: 33 neurosurgeons, 28 neurologists, and 7 other clinicians. Most clinicians were working in the United States (74%), and the remainder (26%) in Canada. All attributes, apart from the nearest location with access to the intervention, influenced preferences significantly. The chance of clinically significant improvement in seizures was the most positive influence on clinician preferences, but low-quality evidence and a higher risk of major complications could offset these preferences. Of the existing neurotechnological interventions, vagus nerve stimulation was predicted to have the highest likelihood of adoption; deep brain stimulation had the lowest likelihood of adoption. SIGNIFICANCE: The preferences of clinicians are drive primarily by the likelihood of achieving seizure freedom for their patients, but preferences for an intervention are largely eradicated if only low quality of evidence supporting the intervention is available. Until better evidence supporting the use of potentially effective, novel neurotechnologies becomes available, clinicians are likely to prefer more established treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".